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MEG

The main interest of the Magnetoencephalography (MEG) Methodology group is the development and implementation of novel soft- and hardware components in the field of MEG research. A major focus is to establish real time data acquisition and signal processing in order to acquire new insights into ongoing electrophysiological brain processes during running MEG measurements. This, however, is a big challenge, since standard MEG data analysis including noise and artifact rejection, source localization and connectivity analysis is computationally demanding and is usually in the range of several days for each single experiment. In combination with neuro-feedback techniques and the application of brain computer interfaces (BCI), real time data analysis will offer a great potential in neuroscience and therapy in neurology.
Another field of attention is dedicated to the development of state-of-the-art analysis tools, such as multivariate connectivity analysis in combination with blind source separation and novel source localization routines.

Projects

Deeplearning

Deep learning-based classification

We propose an artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram (ECG) and electrooculogram (EOG) recordings.

Rauschunterdrückung

Real-time data analysis

In order to decode the spatio-temporal organisation of the human brain in vivo, MEG is an essential tool to study transient changes of electrophysiological processes.

HTc

MEG recordings using High-Temperature SQUIDs

Today almost all MEG systems use low temperature SQUIDs to detect the very small magnetic fields generated by the human brain. Low temperature SQUIDs (LTc) require liquid helium for cooling

Resting state connectivity Analysis

Resting state connectivity Analysis

The study of the neural activity in the resting state has been shown to provide valuable insight into the functional organisation of the human brain.

Neurodynamic progression

Neurodynamic progression

The neurodynamic propagation involves a complex network of communication pathways. The causal relationship between different brain regions is applied in order to investigate the neurodynamics of the network during information processing.

Single trial data analysis

Single trial data analysis

MEG source analysis is usually applied to signal averages revealing the most prominent stereotypic activity. The major drawback is that averaging will not preserve the temporal dynamics of each individual response.

MEG Quellenlokalisierung

Advancements in Forward and Inverse Modeling

Source localization is a central topic in MEG data analysis. Together with the high temporal resolution it is a stronghold of MEG.

Additional Information

Team Leader

Dr. Jürgen Dammers

Staff

Dipl.-Ing. Frank Boers

PhD Harald Chocholacs

PhD Eberhard Eich

M.Sc.Christian Kiefer

Andrea Muren

M.Sc. Praveen Sripad


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